This project includes data Jupyter notebooks for analysis of performance of Active Learning techniques applied to Adaptive Mesh Refinement (AMR) simulations. Specifially, we consider AMR simulations of a shock-bubble interaction phenomenon. We train and evaluate Active Learning algorithms on the data from AMR simulation runs performed on Edison supercomputer at NERSC (US National Energy Research Computing Center).
Contributors:
- Dmitry Duplyakin dmitry.duplyakin@utah.edu
- Jed Brown jed@jedbrown.org
- Donna Calhoun donnacalhoun@boisestate.edu